Electrochemical Quartz Crystal Microbalance Technique to Monitor External Polymeric Pipeline Coatings
Bibliographic record
Abstract
Electrochemical Quartz Crystal Microbalance (EQCM) can be used to monitor changes in several material properties, including mass, electrochemical, viscosity, adsorption, and deposition. In this paper, EQCM was evaluated to determine if it can monitor changes to the metal-coating interface well before any visible∕physical changes to the coating. Modified cathodic disbondment (CD) experiments were carried out over a period of 14 months on 13 coatings used to protect the external surfaces of oil and gas pipelines. During the experiments, EQCM measurements were recorded by placing the quartz crystal at the coating-steel interface. After the experiments, the CD area was measured and compared with the EQCM measurements. Of the 117 panels of 13 different pipeline coatings tested, for 67 % of coatings EQCM correctly predicted the coating performance. Twenty-five percent of coatings performed better than that predicted from EQCM data. Only for 8 % of coatings EQCM measurements predicted better performance but the coatings disbonded during CD area measurement. Based on these observations it was concluded that microscopic changes to the metal-coating interface could be assessed from oscillation frequency of the EQCM crystal placed at the metal-coating interface. However, in order to obtain useful information, extreme care should be exercised in placing the fragile EQCM crystal at the metal-coating interface.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".